Epilepsy MERFISH Report

1. Overview

  • Project: Human Children Epilepsy MERFISH

1.1 Sample Information

A brief sample information is generated from the submission table for the following analysis.

Sample Index and Basic Information
Expt Sample Patient Sample_Region Genotype Region DataPath
1 1300_A7 1300 A7 Epilepsy region_0 Y:_Imaging_data_2\202409191416_20240919Ivy1300A7B12H500ADBCP1407x02_VMSC05201
1 1300_B11 1300 B11 Epilepsy region_1 Y:_Imaging_data_2\202409191416_20240919Ivy1300A7B12H500ADBCP1407x02_VMSC05201
2 41211_TLE 4_12112023 Temporal Lobe Epilepsy region_0 Y:_Imaging_data_2\202409191416_20240919Ivy41211H500ADBCP1407x01_VMSC00101
2 41211_TLE_dup 4_12112023 Temporal Lobe Epilepsy region_1 Y:_Imaging_data_2\202409191416_20240919Ivy41211H500ADBCP1407x01_VMSC00101

1.2 MERSCOPE Data Quality Summary

The summaries present the data quality assessment automatically generated by MERSCOPE for each experiment. We mainly focus on the transcripts level for each sample. So we’re looking for high density in transcripts, based on the transcripts count per field of view (FOV), transcript density in FOV, and frequency of transcripts detected.

Generally, log10 transcript count > 4.0 in most area can be considered as a good quality standard.

Need to note that the low accuracy in DAPI cell boundary is not a concern, as a self-designed cell segmentation processing will take over this task.

1.2.1 (Bad Quality!) 1300_A7, (A7)

1.2.2 1300_B11, (B11)

1.2.3 41211_TLE , (Temporal Lobe)

1.2.4 41211_TLE_dup, (Temporal Lobe)

1.3 Bad Sample Quality Compare

Cell Detection Comapre on Bad Sample

Cell Detection Comapre on Bad Sample

2. Data Processing & Analysis

2.1 Cell Segmentation & Filtering

Based on the spatial information and images obtained from MERFISH, we developed a machine learning model using the Cellpose algorithm to distinguish individual cells via MERFISH DAPI images.

To ensure the data quality and accuracy of cells, we have defined the minimum and maximum values for cell volume and gene count per cell. The cell volume should be between [100, 1800], and the gene count per cell > 25. After filter the outliers, the qualified cells count is shown in the following table.

Outliers were filtered from the data, and the qualified cell count is presented below. The transcript count Violin and transcript count Spatial Map are displayed here as part of the quality control reveal.

2.1.1 Cell Count after Filtering

Cell Total Count After Filtering
Sample Index Cell Count
X1300_B11 22938
X41211_TLE 36727
X41211_TLE_dup 31720

2.1.2 Transcript Count Violin

Transcript Count Violin After Filtering

Transcript Count Violin After Filtering

2.1.3 Transcript Count Spatial Map

Transcript Count Spatial Map After Filtering

Transcript Count Spatial Map After Filtering

2.2 Batch Effect & Dimension Reduction

We use Scanpy for the analysis of single-cell level transcriptome data. The initial stage of our analysis involves the elimination of batch effects, thereby ensuring that different samples from various batches are distributed within the same domain and are statistically reasonable to be integrated and compared. To achieve this, we utilize the Harmony algorithm.

Subsequently, we present visualizations of the batch difference by Leiden UMAP clusters. Also, we illustrate the distributions of the Leiden clusters for future analysis.

Umap of cells and colored by batch

Umap of cells and colored by batch

3. Cell Annotation

We use a recent published tool: Map My Cell to perform cell type annotations for each cell. It is a high resolution cell type annotator build by Alan Institude, with nested levels of classification including 34 classes and 338 subclasses.

The taxonomy is based on the Allen Mouse Brain Common Coordinate Framework version 3 (CCFv3)[https://doi.org/10.1016/j.cell.2020.04.007]. Part of the used abbreviations is list in the supplementary Abbreviation. Otherwise can be found in CCFv3 paper.

With the annotation, we can identify and plot the selected types of cells in each sample.

3.1 Cell Type Umap

3.2 Cell Type Spatial Map

3.3 Glutamatergic Type Spatial Map

3.4 Cell Type Count Table

Cell Type Count
cell_types X1300_B11 X41211_TLE X41211_TLE_dup Total
Astrocytes 3481 3949 3580 11010
Endothelial 1356 1978 1714 5048
Microglia 2850 3680 3096 9626
OPC 1475 1844 1621 4940
Oligo 7628 16074 14267 37969
VLMC 808 1014 900 2722
L2/3/4 IT 542 1874 1603 4019
L5/6 IT 2442 3471 2604 8517
L5/6 EP 141 246 134 521
L6b 826 516 505 1847
LAMP5 197 292 241 730
Pvalb 380 511 424 1315
Sst 475 652 543 1670
Vip 337 626 488 1451
Total 22938 36727 31720 91385

Supplement: Abbreviation

Cell types & Regions

Astro, Astrocyte;

ABC, arachnoid barrier cells;

BAM, border-associated macrophages;

BLA, Basolateral amygdala;

CB, cerebellum;

CGE, caudal ganglionic eminence;

CHOR, choroid plexus;

CNU, cerebral nuclei;

CR, Cajal–Retzius;

CT, corticothalamic;

CTX, cerebral cortex;

CTXsp, cortical subplate;

DC, dendritic cells;

DCO, dorsal cochlear nucleus;

DG, dentate gyrus;

EA, extended amygdala;

Endo, endothelial cells;

ENT, Entorhinal area;

ENTl, Entorhinal area, lateral part;

Epen, ependymal;

EPI, epithalamus;

ET, extratelencephalic;

GC, granule cell;

HB, hindbrain;

HPF, hippocampal formation;

HY, hypothalamus;

HYa, anterior hypothalamic;

IMN, immature neurons;

IT, intratelencephalic;

L6b, layer 6b;

LGE, lateral ganglionic eminence;

LH, lateral habenula;

LSX, lateral septal complex;

MB, midbrain;

MGE, medial ganglionic eminence;

MH, medial habenula;

MM, medial mammillary nucleus;

MY, medulla;

NN, non-neuronal;

NP, near-projecting;

NT, non-telencephalon;

OB, olfactory bulb;

OEC, olfactory ensheathing cells;

OLF, olfactory areas;

Oligo, oligodendrocytes;

OPC, oligodendrocyte precursor cells;

P, pons;

PAL, pallidum;

Peri, pericytes;

PIR, piriform cortex;

SMC, smooth muscle cells;

STR, striatum;

TE, telencephalon;

TH, thalamus;

UBC, unipolar brush cells;

VLMC, vascular leptomeningeal cells.

Neurotransmitter types

Chol, cholinergic;

Dopa, dopaminergic;

GABA, GABAergic;

Glut, glutamatergic;

Glyc, glycinergic;

Hist, histaminergic;

Nora, noradrenergic;

Sero, serotonergic

ADP, anterodorsal preoptic nucleus

AHN, anterior hypothalamic nucleus

ARH, arcuate hypothalamic nucleus

CLI, central linear nucleus raphe

CUN, cuneiform nucleus

DMH, dorsomedial nucleus of the hypothalamus

DMX, dorsal motor nucleus of the vagus nerve

IF, interfascicular nucleus raphe

LHA, lateral hypothalamic area

MDRN, medullary reticular nucleus

MPN, medial preoptic nucleus

MPO, medial preoptic area

MV, medial vestibular nucleus

NTS, nucleus of the solitary tract

PAG, periaqueductal grey

PARN, parvicellular reticular nucleus

PB, parabrachial nucleus

PBG, parabigeminal nucleus

PGRN, paragigantocellular reticular nucleus

PGRNd, paragigantocellular reticular nucleus, dorsal part

PH, posterior hypothalamic nucleus

PMv, ventral premammillary nucleus

PPN, pedunculopontine nucleus

PVa, periventricular hypothalamic nucleus, anterior part

PVHd, paraventricular hypothalamic nucleus, descending division

PVi, periventricular hypothalamic nucleus, intermediate part

PVpo, periventricular hypothalamic nucleus, preoptic part

PVR, periventricular region

RAmb, midbrain raphe nuclei

RL, rostral linear nucleus raphe

SBPV, subparaventricular zone

SNc, substantia nigra, compact part

SPIV, spinal vestibular nucleus

TMv, tuberomammillary nucleus, ventral part

VII, facial motor nucleus

VMPO, ventromedial preoptic nucleus

VTA, ventral tegmental area

ZI, zona incerta.